A new approach for heat flux estimation in composite materials
An important process for enhancing the productivity of different applications is Heat Flux (HF) distribution estimation. Accurate HF estimation led to the importance of effective thermal management in different applications. However, none of the prevailing research works concentrated on estimating t...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025004505 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850086927420620800 |
|---|---|
| author | Mohammad Saraireh |
| author_facet | Mohammad Saraireh |
| author_sort | Mohammad Saraireh |
| collection | DOAJ |
| description | An important process for enhancing the productivity of different applications is Heat Flux (HF) distribution estimation. Accurate HF estimation led to the importance of effective thermal management in different applications. However, none of the prevailing research works concentrated on estimating the HF for different composite materials, which led to the production of low-quality material during application. Thus, this paper proposes a novel HF distribution using the Finite Element Analysis (FEA) model and proposed classifier approach. For the analysis, the metal, reinforced concrete, and ceramic matrix composite materials are utilized. Initially, the boundary condition and mesh generation are carried out. Next, Computational Fluid Dynamics (CFD) modeling is processed. Then, with the help of Carslaw and Jaeger Contour Plot Construction (CJCPC), the values are visualized. Then, from the visualized plot, the peak values are extracted. Then, the HF distribution is estimated with the help of data generated from FEA. According to the material, the input data are clustered for the estimation. Now, the labeling process is carried out from the extracted features using the Fisher Membership Function-based Fuzzy Inference System (FMF-FIS), and the Entropy Production Rate (EPR) is estimated. Finally, the uniform and non-uniform HF distribution is classified. Thus, the similar composite material is clustered with a clustering time of 28734 ms, and the labeling of HF is done with a fuzzification time of 673 ms. Also, the HF distribution is estimated with a training time of 43922 ms and accuracy of 99.57 %, thus proving better performance than existing works. |
| format | Article |
| id | doaj-art-a7d266c0326f4e328a59d5d5fb44ef9f |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-a7d266c0326f4e328a59d5d5fb44ef9f2025-08-20T02:43:20ZengElsevierResults in Engineering2590-12302025-03-012510437110.1016/j.rineng.2025.104371A new approach for heat flux estimation in composite materialsMohammad Saraireh0Mechanical Engineering Department, Faculty of Engineering, Mutah University, Karak 61710, JordanAn important process for enhancing the productivity of different applications is Heat Flux (HF) distribution estimation. Accurate HF estimation led to the importance of effective thermal management in different applications. However, none of the prevailing research works concentrated on estimating the HF for different composite materials, which led to the production of low-quality material during application. Thus, this paper proposes a novel HF distribution using the Finite Element Analysis (FEA) model and proposed classifier approach. For the analysis, the metal, reinforced concrete, and ceramic matrix composite materials are utilized. Initially, the boundary condition and mesh generation are carried out. Next, Computational Fluid Dynamics (CFD) modeling is processed. Then, with the help of Carslaw and Jaeger Contour Plot Construction (CJCPC), the values are visualized. Then, from the visualized plot, the peak values are extracted. Then, the HF distribution is estimated with the help of data generated from FEA. According to the material, the input data are clustered for the estimation. Now, the labeling process is carried out from the extracted features using the Fisher Membership Function-based Fuzzy Inference System (FMF-FIS), and the Entropy Production Rate (EPR) is estimated. Finally, the uniform and non-uniform HF distribution is classified. Thus, the similar composite material is clustered with a clustering time of 28734 ms, and the labeling of HF is done with a fuzzification time of 673 ms. Also, the HF distribution is estimated with a training time of 43922 ms and accuracy of 99.57 %, thus proving better performance than existing works.http://www.sciencedirect.com/science/article/pii/S2590123025004505Finite element analysisNeural networkBoundary conditionDeep learning |
| spellingShingle | Mohammad Saraireh A new approach for heat flux estimation in composite materials Results in Engineering Finite element analysis Neural network Boundary condition Deep learning |
| title | A new approach for heat flux estimation in composite materials |
| title_full | A new approach for heat flux estimation in composite materials |
| title_fullStr | A new approach for heat flux estimation in composite materials |
| title_full_unstemmed | A new approach for heat flux estimation in composite materials |
| title_short | A new approach for heat flux estimation in composite materials |
| title_sort | new approach for heat flux estimation in composite materials |
| topic | Finite element analysis Neural network Boundary condition Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025004505 |
| work_keys_str_mv | AT mohammadsaraireh anewapproachforheatfluxestimationincompositematerials AT mohammadsaraireh newapproachforheatfluxestimationincompositematerials |